Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add event4u-app/agent-config --skill brand-auditgit clone --depth 1 https://github.com/event4u-app/agent-configWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/event4u-app/agent-config/brand-audit)<a href="https://agentmods.dev/skills/event4u-app/agent-config/brand-audit"><img src="https://agentmods.dev/badge/skills/event4u-app/agent-config/brand-audit/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/event4u-app/agent-config/brand-audit"><img src="https://agentmods.dev/badge/skills/event4u-app/agent-config/brand-audit.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00039 | $0.01508 |
| Opus 5 | $0.00019 | $0.00754 |
| Sonnet 5 | $0.00008 | $0.00302 |
| Haiku 4.5 | $0.00004 | $0.00151 |
Grade A, and why
brand-audit scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 104 lines — stays where its author put it; the contents beside it link to each section on GitHub.
brand-audit
Method skill. Inventories current brand expression across touchpoints and flags drift against defined brand tokens, voice, and strategy. For the UI surface it leans on existing-ui-audit rather than re-implementing component inventory. Output is a drift findings list — not a redesign.
When to use
- Before a rebrand or brand refresh — establish the baseline first.
- When auditing brand consistency across touchpoints (web, decks, docs, ads, copy).
- To create an evidence base before running
brand-strategy. - When "is this on-brand?" needs a systematic answer across many assets, not a gut call.
Procedure
-
Gather the source of truth. Collect the consumer brand's defined tokens (palette, type scale, logo rules), voice profile, and strategy doc if they exist. Consumer brand definition is authoritative; corpus defaults are gap-fill only.
-
Inventory current expression per touchpoint. Cover logo usage, colour palette, typography, voice/copy tone, imagery style, and iconography across the relevant surfaces (site, app, decks, marketing, docs).
Personal and company profile surfaces, including LinkedIn, X, and GitHub organization profiles, are deliberately excluded regardless of whether their content is fetched, pasted, or supplied as a file, pending a written public-profile-field privacy floor defining what may be processed.
Recorded so the next reader does not re-derive the gap, which has already happened once. The exclusion is about handling, not retrieval: a pasted profile is mechanically a different thing from a network fetch, but the missing artefact is a field-level classification saying which profile fields are brand surface (logo, palette, bio messaging) and which are PII-adjacent (employment history, contributor names, location). A profile's About text carries both in one field. Without that classification the skill cannot distinguish them, and no test can assert that it did. AI council 2/2, 2026-08-25. Include user-supplied profile inputs once a written, testable field-level privacy floor exists; include fetched inputs only after both that floor and a host-agent fetch contract exist.
-
UI surface. Invoke
existing-ui-auditfor UI component inventory. Do not re-implement it here — take its output as an input to this audit. -
Compare observed vs. defined. For each touchpoint value, check it against the matching token or voice rule.
-
Classify each finding. Three buckets:
on-brand(matches the defined token),drift(observed value diverges from the token),undefined(no token exists to audit against — this is a governance gap, not automatically wrong). -
Rank drift findings by visibility (how prominent the touchpoint is) multiplied by frequency (how often it appears). Surface the top items first.
-
Verify completeness. Confirm every inventoried touchpoint is classified (
on-brand/drift/undefined) — the audit is complete only when the classified count equals the inventoried count, andexisting-ui-audithas run for every in-scope UI surface. Ensure each drift finding cites BOTH the defined value and the observed value; a finding missing either side is not yet verified. -
Output the findings list (see Output format). Do not redesign or author replacements — hand drift findings to
brand-identityorbrand-strategy.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 104 lines · 39 tokens per session scan A 95f022c4460d
brand-audit is a skill published in the GitHub repository event4u-app/agent-config (10 stars, last pushed today), licensed MIT. It adds 39 tokens to every session and 1,508 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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